Hybrid GSA-PSO MPPT for Partially Shaded PV in Grid-connected PV System

Solar photovoltaic (PV) generation, as a renewable energy source, emerges as a viable alternative to conventional energy systems due to its vast availability and environmentally sustainable characteristics. Maximizing power extraction from PV systems is essential for improving solar energy efficiency, particularly under partial shading conditions (PSCs). Consequently, an efficient maximum power point tracking (MPPT) technique is imperative for accurately identifying the maximum power point (MPP), even in the presence of PSCs. Under standard conditions, the power-voltage (P-V) curve exhibits a single peak, simplifying MPP tracking. However, under PSCs, the P-V curve generates multiple peaks, consisting of one global maximum and several local maxima. Conventional MPPT methods often struggle to locate the true MPP under such conditions, necessitating the development of more advanced MPPT techniques. This study proposes an MPPT controller integrating the Gravity Search Algorithm (GSA) and Particle Swarm Optimization (PSO) to enhance tracking performance in PV systems operating under PSCs. Furthermore, this research aligns with the United Nations Sustainable Development Goal (SDG) 7, advocating for affordable and clean energy access. The effectiveness of the proposed hybrid algorithm is evaluated against the conventional Perturb and Observe (PO) algorithm. The findings indicate that the hybrid approach achieves superior tracking efficiency and faster response time compared to traditional MPPT techniques.